@article{MAKHILLJEAS2017122114960,
    title = {Efficient Data Clustering Algorithm Designed for 2-D Dataset},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {21},
    pages = {5485-5489},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.5485.5489},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.5485.5489},
    author = {Himanika,Vishesh and},
    keywords = {Clustering,partition,hierarchical,agglomerative,divisive,k-means},
    abstract = {Extraction of information from a database is a major issue these days. There is huge amount of
information available in web in the form of web pages which is used to extract as per the need of the user to
perform a vital task. To overcome this issue of information retrieval various techniques are known today like
clustering, classification, natural language processing techniques etc. In this study, we have discussed various
clustering methods algorithms with various features to classify the data. k-means clustering algorithm is majorly
used to cluster the data which is also focussed in this study. The capability of k-means clustering algorithm
is due to its computational competence. k-means is a clustering technique in which similar data points are
grouped into clusters. In this study, we have proposed a clustering algorithm based on the density of data
points and used Manhattan distance for grouping the data points into a cluster. It has been empirically found
that the results of proposed clustering algorithm provide better clusters as compared to existing clustering
algorithms.}
    }